SoroushVahidi/scidocs huggingface dataset:
Natural Language Interface to Relational Database (NLI-RDB) Through Object Relational Mapping (ORM)
(2016)

This paper proposes a novel approach for building a Natural Language Interface to a Relational Database (NLI-RDB) using Conversational Agent (CA), Information Extraction (IE) and Object Relational Mapping (ORM) framework. The CA will help in disambiguating the user’s queries and guiding the user interaction. IE will play an important role in named entities extraction in order to map Natural Language queries into database queries. The ORM framework i.e. the Hibernate framework resolves the impedance mismatch between the Object Oriented Paradigms (OOP) and Relational Databases (RDBs) i.e. OOP concepts differ from RDB concepts, thus it reduces the complexity in generating SQL statements. Also, by utilizing ORM framework, the RDBs entities are mapped into real world objects, which bring the RDBs a step closer to the user. In addition, the ORM framework simplify the interaction between OOP and RDBs. The developed NLI-RDB system allows the user to interact with objects directly in natural language and through navigation, rather than by using SQL statements. This direct interaction tends to be easier and more acceptable for humans whom are nor technically orientated and have no SQL knowledge. The NLI-RDB system also offers friendly and interactive user interface in order to refine the query generated automatically. The NLI-RDB system has been evaluated by a group of participants through a combination of qualitative and quantitative measures. The experimental results show good performance of the prototype and excellent user’s satisfaction.
Artificial intelligence applications in the intensive care unit.
(2001)

OBJECTIVE
To review the history and current applications of artificial intelligence in the intensive care unit.


DATA SOURCES
The MEDLINE database, bibliographies of selected articles, and current texts on the subject.


STUDY SELECTION
The studies that were selected for review used artificial intelligence tools for a variety of intensive care applications, including direct patient care and retrospective database analysis.


DATA EXTRACTION
All literature relevant to the topic was reviewed.


DATA SYNTHESIS
Although some of the earliest artificial intelligence (AI) applications were medically oriented, AI has not been widely accepted in medicine. Despite this, patient demographic, clinical, and billing data are increasingly available in an electronic format and therefore susceptible to analysis by intelligent software. Individual AI tools are specifically suited to different tasks, such as waveform analysis or device control.


CONCLUSIONS
The intensive care environment is particularly suited to the implementation of AI tools because of the wealth of available data and the inherent opportunities for increased efficiency in inpatient care. A variety of new AI tools have become available in recent years that can function as intelligent assistants to clinicians, constantly monitoring electronic data streams for important trends, or adjusting the settings of bedside devices. The integration of these tools into the intensive care unit can be expected to reduce costs and improve patient outcomes.
Design of a Wideband Waveguide Slot Array Antenna and its Decoupling Method for Synthetic Aperture Radar
(2008)

For the synthetic aperture radar systems, there is an increasing demand for achieving resolutions of 0.2 mtimes0.2 m. As the range resolution and system bandwidth are inversely proportional, the system bandwidth is expected to be greater than 1 GHz. Thus an antenna with wider band needs to be developed. Waveguide slot antennas have been implemented on several SAR satellites due to its inherent advantages such as high efficiency and power handling capacity, but its bandwidth is quite limited. To avoid the manufacture difficulties of the ridge waveguide, which is capable of broadening the bandwidth of slot antennas, a novel antenna element with conventional waveguide is designed. The bandwidth of VSWR les1.5 is greater than 1 GHz at X-band. To reduce the mutual coupling of closely placed antenna elements, a decoupling method with cavity-like walls inserted between adjacent elements is adopted and their effects on the performance of the antenna are summarized.
Identification of a DNA-binding site for the transcription factor Haa1, required for Saccharomyces cerevisiae response to acetic acid stress
(2011)

The transcription factor Haa1 is the main player in reprogramming yeast genomic expression in response to acetic acid stress. Mapping of the promoter region of one of the Haa1-activated genes, TPO3, allowed the identification of an acetic acid responsive element (ACRE) to which Haa1 binds in vivo. The in silico analysis of the promoter regions of the genes of the Haa1-regulon led to the identification of an Haa1-responsive element (HRE) 5'-GNN(G/C)(A/C)(A/G)G(A/G/C)G-3'. Using surface plasmon resonance experiments and electrophoretic mobility shift assays it is demonstrated that Haa1 interacts with high affinity (K(D) of 2 nM) with the HRE motif present in the ACRE region of TPO3 promoter. No significant interaction was found between Haa1 and HRE motifs having adenine nucleotides at positions 6 and 8 (K(D) of 396 and 6780 nM, respectively) suggesting that Haa1p does not recognize these motifs in vivo. A lower affinity of Haa1 toward HRE motifs having mutations in the guanine nucleotides at position 7 and 9 (K(D) of 21 and 119 nM, respectively) was also observed. Altogether, the results obtained indicate that the minimal functional binding site of Haa1 is 5'-(G/C)(A/C)GG(G/C)G-3'. The Haa1-dependent transcriptional regulatory network active in yeast response to acetic acid stress is proposed.
Analyzing spatial user behavior in computer games using geographic information systems
(2009)

An important aspect of the production of digital games is user-oriented testing. A central problem facing practitioners is however the increasing complexity of user-game interaction in modern games, which places challenges on the evaluation of interaction using traditional user-oriented approaches. Gameplay metrics are instrumentation data which detail user behavior within the virtual environment of digital games, forming accurate and detailed datasets about user behavior that supplement existing user-testing methods such as playtesting and usability testing. In this paper existing work on gameplay metrics is reviewed, and spatial analysis of gameplay metrics introduced as a new approach in the toolbox of user-experience testing and research. Furthermore, Geographic Information Systems (GIS) are introduced as a tool for performing spatial analysis. A case study is presented with Tomb Raider: Underworld, showcasing the merger of GIS with gameplay metrics analysis and its application to game testing and design.
Nanoseconds Switching for High Voltage Circuit Using Avalanche Transistors
(2009)

The avalanche transistor is suitable for switching high voltage in kilovolts region. In this paper, a simple switching circuit consists of avalanche transistor is demonstrated. Sixteen of ZTX 415 avalanche transistors were used to switch a high voltage circuit up to 4.5 kV. A PIC16F84 microcontroller was utilized as control unit to generate input trigger. The result shows that the developed circuit was able to switch applied voltage up-to 4.5 kV with an average falling time is 2.89 ns.
Signal Processing and Machine Learning with Differential Privacy: Algorithms and Challenges for Continuous Data
(2013)

Private companies, government entities, and institutions such as hospitals routinely gather vast amounts of digitized personal information about the individuals who are their customers, clients, or patients. Much of this information is private or sensitive, and a key technological challenge for the future is how to design systems and processing techniques for drawing inferences from this large-scale data while maintaining the privacy and security of the data and individual identities. Individuals are often willing to share data, especially for purposes such as public health, but they expect that their identity or the fact of their participation will not be disclosed. In recent years, there have been a number of privacy models and privacy-preserving data analysis algorithms to answer these challenges. In this article, we will describe the progress made on differentially private machine learning and signal processing.
Overview of the ImageCLEF 2016 Medical Task
(2016)

ImageCLEF is the image retrieval task of the Conference and Labs of the Evaluation Forum (CLEF). ImageCLEF has historically focused on the multimodal and language–independent retrieval of images. Many tasks are related to image classification and the annotation of image data as well. The medical task has focused more on image retrieval in the beginning and then retrieval and classification tasks in subsequent years. In 2016 a main focus was the creation of meta data for a collection of medical images taken from articles of the the biomedical scientific literature. In total 8 teams participated in the four tasks and 69 runs were submitted. No team participated in the caption prediction task, a totally new task. Deep learning has now been used for several of the ImageCLEF tasks and by many of the participants obtaining very good results. A majority of runs was submitting using deep learning and this follows general trends in machine learning. In several of the tasks multimodal approaches clearly led to best results.
Recognizing independent and joint activities among multiple residents in smart environments
(2010)

The pervasive sensing technologies found in smart homes offer unprecedented opportunities for providing health monitoring and assistance to individuals experiencing difficulties living independently at home. A primary challenge that needs to be tackled to meet this need is the ability to recognize and track functional activities that people perform in their own homes and everyday settings. In this paper, we look at approaches to perform real-time recognition of Activities of Daily Living. We enhance other related research efforts to develop approaches that are effective when activities are interrupted and interleaved. To evaluate the accuracy of our recognition algorithms we assess them using real data collected from participants performing activities in our on-campus smart apartment testbed.
Definition Modeling: Learning to define word embeddings in natural language
(2017)

Distributed representations of words have been shown to capture lexical semantics, as demonstrated by their effectiveness in word similarity and analogical relation tasks. But, these tasks only evaluate lexical semantics indirectly. In this paper, we study whether it is possible to utilize distributed representations to generate dictionary definitions of words, as a more direct and transparent representation of the embeddings’ semantics. We introduce definition modeling, the task of generating a definition for a given word and its embedding. We present several definition model architectures based on recurrent neural networks, and experiment with the models over multiple data sets. Our results show that a model that controls dependencies between the word being defined and the definition words performs significantly better, and that a characterlevel convolution layer designed to leverage morphology can complement word-level embeddings. Finally, an error analysis suggests that the errors made by a definition model may provide insight into the shortcomings of word embeddings.
Message Passing Inference for Large Scale Graphical Models with High Order Potentials
(2014)

To keep up with the Big Data challenge, parallelized algorithms based on dual decomposition have been proposed to perform inference in Markov random fields. Despite this parallelization, current algorithms struggle when the energy has high order terms and the graph is densely connected. In this paper we propose a partitioning strategy followed by a message passing algorithm which is able to exploit pre-computations. It only updates the high-order factors when passing messages across machines. We demonstrate the effectiveness of our approach on the task of joint layout and semantic segmentation estimation from single images, and show that our approach is orders of magnitude faster than current methods.
Increasing emitter efficiency in 3.3-kV enhanced trench IGBTs for higher short-circuit capability
(2018)

In this paper, a 3.3-kV Enhanced Trench IGBT has been designed with a high emitter efficiency, for improving its short-circuit robustness. The carrier distribution profile has been shaped in a way that it is possible to increase the electric field at the surface of the IGBT, and thereby, counteract the Kirk Effect onset. This design approach is beneficial for mitigating high-frequency oscillations, typically observed in IGBTs under short-circuit conditions. The effectiveness of the proposed design rule is validated by means of mixed-mode device simulations. Then, two IGBTs have been fabricated with different emitter efficiencies and tested under short circuit, validating that the high-frequency oscillations can be mitigated, with higher emitter efficiency IGBT designs.
Transparent authentication systems for mobile device security: A review
(2015)

Sensitive data such as text messages, contact lists, and personal information are stored on mobile devices. This makes authentication of paramount importance. More security is needed on mobile devices since, after point-of-entry authentication, the user can perform almost all tasks without having to re-authenticate. For this reason, many authentication methods have been suggested to improve the security of mobile devices in a transparent and continuous manner, providing a basis for convenient and secure user re-authentication. This paper presents a comprehensive analysis and literature review on transparent authentication systems for mobile device security. This review indicates a need to investigate when to authenticate the mobile user by focusing on the sensitivity level of the application, and understanding whether a certain application may require a protection or not.
Cayuga : A High-Performance Event Processing Engine ∗ [ Demonstration Paper ]
(2007)

We propose a demonstration of Cayuga, a complex event monitoring system for high speed data streams. Our demonstration will show Cayuga applied to monitoring Web feeds; the demo will illustrate the expressiveness of the Cayuga query language, the scalability of its query processing engine to high stream rates, and a visualization of the internals of the query processing engine.
General Sales Forecast Models for Automobile Markets and their Analysis
(2012)

In this paper, various enhanced sales forecast methodologies and models for the automobile market are presented. The methods used deliver highly accurate predictions while maintaining the ability to explain the underlying model at the same time. The representation of the economic training data is discussed, as well as its effects on the newly registered automobiles to be predicted. The methodology mainly consists of time series analysis and classical Data Mining algorithms, whereas the data is composed of absolute and/or relative market-specific exogenous parameters on a yearly, quarterly, or monthly base. It can be concluded that the monthly forecasts were especially improved by this enhanced methodology using absolute, normalized exogenous parameters. Decision Trees are considered as the most suitable method in this case, being both accurate and explicable. The German and the US-American automobile market are presented for the evaluation of the forecast models.
Quality expectations of machine translation
(2018)

Machine Translation (MT) is being deployed for a range of use-cases by millions of people on a daily basis. There should, therefore, be no doubt as to the utility of MT. However, not everyone is convinced that MT can be useful, especially as a productivity enhancer for human translators. In this chapter, I address this issue, describing how MT is currently deployed, how its output is evaluated and how this could be enhanced, especially as MT quality itself improves. Central to these issues is the acceptance that there is no longer a single ‘gold standard’ measure of quality, such that the situation in which MT is deployed needs to be borne in mind, especially with respect to the expected ‘shelf-life’ of the translation itself. 1 Machine Translation Today Machine Translation (MT) is being deployed for a range of use-cases by millions of people on a daily basis. I will examine the reasons for this later in this chapter, but one inference is very clear: those people using MT in those use-cases must already be satisfied with the level of quality emanating from the MT systems they are deploying, otherwise they would stop using them. That is not the same thing at all as saying that MT quality is perfect, far from it. The many companies and academic researchers who develop and deploy MT engines today continue to strive to improve the quality of the translations produced. This too is an implicit acceptance of the fact that the level of quality is sub-optimal – for some use-cases at least – and can be improved. If MT system output is good enough for some areas of application, yet at the same time system developers are trying hard to improve the level of translations produced by their engines, then translation quality – whether produced by a machine or by a human – needs to be measurable. Note that this applies also to translators who complain that MT quality is too poor to be used in their workflows; in order to decide that with some certainty – rather than rejecting MT out-of-hand merely as a knee-jerk reaction to the onset of this new technology – the impact of MT on translators’ work needs to be measurable. In Way (2013), I appealed to two concepts, which are revisited here, namely:
Fuzzy Queries over NoSQL Graph Databases: Perspectives for Extending the Cypher Language
(2014)

When querying databases, users often wish to express vague concepts, as for instance asking for the cheap hotels. This has been extensively studied in the case of relational databases. In this paper, we propose to study how such useful techniques can be adapted to NoSQL graph databases where the role of fuzziness is crucial. Such databases are indeed among the fastest-growing models for dealing with big data, especially when dealing with network data (e.g., social networks). We consider the Cypher declarative query language proposed for Neo4j which is the current leader on this market, and we present how to express fuzzy queries.
Are image quality metrics adequate to evaluate the quality of geometric objects?
(2001)

Geometric objects are often represented by many millions of triangles or polygons, which limits the ease with which they can be transmitted and displayed electronically. This has led to the development of many algorithms for simplifying geometric models, and to the recognition that metrics are required to evaluate their success. The goal is to create computer graphic renderings of the object that do not appear to be degraded to a human observer. The perceptual evaluation of simplified objects is a new topic. One approach has been to use image-based metrics to predict the perceived degradation of simplified 3-D models 1 Since that 2-D images of 3-D objects can have significantly different perceived quality, depending on the direction of the illumination, 2 2-D measures of image quality may not adequately capture the perceived quality of 3-D objects. To address this question, we conducted experiments in which we explicitly compared the perceived quality of animated 3-D objects and their corresponding 2-D still image projections. Our results suggest that 2-D judgements do not provide a good predictor of 3-D image quality, and identify a need to develop “object quality metrics.”
Motor Bearing Fault Diagnosis Using Trace Ratio Linear Discriminant Analysis
(2014)

Bearings are critical components in induction motors and brushless direct current motors. Bearing failure is the most common failure mode in these motors. By implementing health monitoring and fault diagnosis of bearings, unscheduled maintenance and economic losses caused by bearing failures can be avoided. This paper introduces trace ratio linear discriminant analysis (TR-LDA) to deal with high-dimensional non-Gaussian fault data for dimension reduction and fault classification. Motor bearing data with single-point faults and generalized-roughness faults are used to validate the effectiveness of the proposed method for fault diagnosis. Comparisons with other conventional methods, such as principal component analysis, local preserving projection, canonical correction analysis, maximum margin criterion, LDA, and marginal Fisher analysis, show the superiority of TR-LDA in fault diagnosis.
GPGPU Enabled Ray Directed Adaptive Volume Visualization for High Density Scans
(2018)

This paper presents an open source implementation of a volume visualizer capable of rendering large scale simulated and tomographic volume datasets on everyday commodity hardware. The ability to visualize this data enables a wide range of analysis in many scientific disciplines, such as medicine, biology, engineering, and physics. The implementation presented takes advantage of a hierarchical space filling curve to efficiently query only a subset of the data required for accurate visualization. A major advantage of this implementation is that it is open source, and works on a wide range of visualization devices by using the Unity game engine, a cross platform engine capable of targeting over 25 platforms [24]. This work's unique contribution is utilizing a space filling curve for multiple bricks in the volume to permit adaptive streaming at different resolutions.
A real-time grading method of apples based on features extracted from defects
(2003)

This paper presents a hierarchical grading method applied to Jonagold apples. Several images covering the whole surface of the fruits were acquired thanks to a prototype grading machine. These images were then segmented and the features of the defects were extracted. During a learning procedure, the objects were classified into clusters by k-mean clustering. The classification probabilities of the objects were summarised and on this basis the fruits were graded using quadratic discriminant analysis. The fruits were correctly graded with a rate of 73 %. The errors were found having origins in the segmentation of the defects or for a particular wound, in a confusion with the calyx end.
Immuno-inspired autonomic system for cyber defense
(2007)

The biological immune system is an autonomic system for self-protection, which has evolved over millions of years probably through extensive redesigning, testing, tuning and optimization process. The powerful information processing capabilities of the immune system, such as feature extraction, pattern recognition, learning, memory, and its distributive nature provide rich metaphors for its artificial counterpart. Our study focuses on building an autonomic defense system, using some immunological metaphors for information gathering, analyzing, decision making and launching threat and attack responses. This on-going research effort is not to mimic the nature but to explore and learn valuable lessons useful for self-adaptive cyber defense systems.